Artificial Intelligence Powered Strategies for Well Log Analysis: Quality Control, Editing, and Data Reconstruction

Abstract Well log data is critical in hydrocarbon exploration and production, providing detailed information about subsurface geological formations. Accurate interpretation of this data is essential for identifying hydrocarbon-bearing zones, optimizing well placement, and ensuring effective reservoir management. However, the presence of outliers and missing values in well log datasets can severely compromise the accuracy and reliability of geological interpretations. Traditional methods for handling these data issues, such as z-scores and simple interpolation, often fall short in capturing the complex patterns inherent in the data. In this study, advanced machine learning techniques were applied to improve the quality of well log data. Isolation Forest algorithm was employed to detect and remove outliers, leveraging its ability to efficiently isolate anomalies in high-dimensional datasets. For handling missing data, Random Forest algorithm was used to predict and impute missing values, utilizing its ensemble learning capability to capture non-linear relationships between variables. Through these machine learning methods, significant improvements were observed in the data's accuracy, reliability, and completeness. The results showed that Isolation Forest algorithm successfully identified and eliminated outliers in key logs such as Sonic (DT), Density (RHOB), and Neutron (NPHI), while Random Forest algorithm achieved high predictive accuracy for missing values. This approach not only enhances the integrity of the well log data but also facilitates more accurate geological and petrophysical interpretations. The study's findings demonstrate the potential of machine learning techniques in transforming traditional well log data processing workflows, leading to more informed and efficient decision-making in hydrocarbon exploration and production.